Become a Big Data Hadoop Developer from scratch
3.8 (10 ratings)
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Become a Big Data Hadoop Developer from scratch

Basic Hadoop tutorial
3.8 (10 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
105 students enrolled
Last updated 9/2016
English
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Current price: $10 Original price: $20 Discount: 50% off
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Includes:
  • 5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • learn introduction to hadoop
  • Hadoop Distributed File System(HDFS)
  • MapReduce(MR)
  • Run MapReduce Application using JAVA
  • Run Word Count example in JAVA
  • Run Max.Temp. Hadoop MapReduce program in JAVA
  • HDFS Commands for accessing Hadoop File System
  • Running Queries in HBase
  • Different operations in HBase using JAVA API
  • HBase Architecture
  • Apache HIVE
  • Apache PIG
View Curriculum
Requirements
  • Basics of Object Oriented Programming
  • Basics of JAVA
  • Knowledge of programming would be beneficial but not mandatory
Description

Apache Hadoop is an open-source software framework for distributed storage and distributed processing of large data on computer clusters built from commodity hardware.

In this course we'll discuss about several important aspects of Hadoop like HDFS(Hadoop Distributed File System), MapReduce, Hive, HBase and Pig.

First we'll talk about Overview of Big data means what is Big Data, Facts of Big Data, Scenarios, Hadoop cluster architecture. Then we'll move towards HDFS, Components of HDFS and its architecture, NameNode, Secondary NameNode and DataNode.

Next module is about MapReduce. In this we'll talk about Map Phase and Reduce Phase, Architecture of MapReduce, Combiners and Reducers. 

Next module is about PIG. In this we'll see what is Apache Pig, its importance, Pig Latin language, and where to avoid Pig.

Them we'll talk about HBase, we'll talk about its use cases, general commands in HBase, DDL in HBase, DML in HBase, How to create, delete and integrate table in HBase and lot more.

So start learning Hadoop today.

Who is the target audience?
  • Professionals who want to learn Hadoop
  • Data Analyst
  • Hadoop Beginners
  • Professionals who want to make MapReduce Application
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Curriculum For This Course
82 Lectures
05:02:24
+
Module1
9 Lectures 22:35


1.3 Big Data Scenarios
02:16

1.4 Introduction to Hadoop
03:07

1.7 Difference between RDBMS and Hadoop
01:15

1.8 Cluster Modes in Hadoop
01:00

1.9 Hadoop Ecosystem
03:40

1.10 HDFS Daemons and Mapreduce daemons
02:18

1.11 HADOOP CLUSTER ARCHITECTURE
02:28
+
Module 2
6 Lectures 21:13
2.1 HDFS
02:19

2.2 HDFS FILES AND BLOCKS
03:40

2.3 HDFS Components and Architecture
03:14

2.4 NameNode Secondary NameNode and DataNode
03:34

2.5 HDFS FILE READ AND WRITE OPERATIONS
03:49

HDFS Commands
04:37
+
Module 3
10 Lectures 41:52

3.2 Map Phase and Reduce Phase
02:27

3.3 JOB Submission Flow in MapReduce
02:43

3.4 HDFS BLOCK AND INPUTSPLIT
02:00

3.5 MAPREDUCE ARCHITECTURE
03:02

3.6 COMBINER and REDUCERS
03:39

3.7 MapReduce DataFlow
02:00

Wordcount Prgram in MapReduce
11:07

Maximum Temprature Program part-1
05:00

Maximum Temprature Program part-2
03:56
+
Module 4
8 Lectures 11:39
4.1 Map, Reduce and Driver
01:04

4.2 Difference between New API and Old API
02:12

4.3 GenericOptionsParser, Tool and ToolRunner
02:08

4.4 Writables in Hadoop
01:08

4.5 Serialization and Deserialization in Hadoop
01:08

4.6 Schedulers and Distributed Cache
01:20

4.7 Sequence File
01:32

4.9 MRUNIT TESTING
01:07
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Module 5
7 Lectures 12:46
5.1 Apache Pig
01:27

5.2 Importance of Apache PIG
01:17

5.3 Apache Pig vs MapReduce
01:45

5.4 Where Pig is Best Suited
01:37

5.5 Where to avoid Pig
00:28

5.6 PIG Latin Language
03:50

5.7 Running PIG in Different Modes
02:22
+
Module 6
42 Lectures 03:12:19
6.1 What is HBase
01:31

6.2 UseCases of Apache PIG
02:53

6.3 What is NoSQL Databases
01:00

6.4 Characteristics of NoSQL Databases
01:45

6.5 Categories of NoSQL Databases
03:09

6.6 Difference between NoSQL and RDBMS
03:29

6.7 Characteristics of Apache HBase
01:42

6.8 Comparison between HDFS and HBase
02:23

6.9 Where to use HBase
01:10

6.10 Where to avoid HBase
01:30

6.11 Building Blocks of Apache HBase
03:21

6.12 Column Family in HBase
03:36

6.13 Storage of Column Family
01:43

6.14 TimeStamp as Version
03:44

6.15 General Commands in HBase
06:06

6.16 DDL in HBase
05:06

6.17 DDL in HBase-2
05:22

6.18 DML in HBase
07:48

6.19 Core Components of HBase
03:08

6.20 Need of Zookeeper
04:32

6.21 How Client interacts with HBase Cluster
02:54

6.22 Bloom Filter in HBase
02:52

6.23 HFile in HBase
04:11

6.24 Write Ahead Log in HBase
04:29

6.25 Creating Table in Apache HBase Using JAVA API
11:53

6.26 Deleating Table in Apache HBase Using JAVA API
03:42

6.27 Disabling Table in Apache HBase Using JAVA API
03:13

6.28 Inserting data in Table in Apache HBase Using JAVA API
06:56

6.29 How to list and Enable a table in Apache HBase Using JAVA API
04:37

6.30 Integrating Apache HBase with Pig Tool part-I
07:05

6.31 Integrating HBase with Apache Pig -II
06:24

6.32 Integrating HBase with Apache PIG-III
10:46

6.33 Integrating HBase with Apache HIve
06:51

6.34 Integration of Hive Tool with Apache HBase
12:42

6.35 Integrating HBase with Apache Sqoop
05:31

6.36 Integration of Apache HBase with Sqoop Tool part-2
08:56

6.37 Filtering Operations in HBase
02:46

6.38 KeyOnlyFilter in HBase
07:49

6.39 FirstKeyOnlyFilter and PrefixFilter in HBase
03:46

6.40 ColumnCountGetFilter and PageFilter
02:39

6.42 ColumnPrefixFilter and MultipleColumnPrefixFilters
05:03

6.43 ValueFilter
02:16
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